Llama 3.3 70B Instruct
Meta's efficiency model. Context window: 131K tokens.
Scores by test
Methodology →What you need to know
Llama 3.3 70B Instruct is most effective as a high-capacity utility model for long-context processing and structured data tasks. With a perfect 5/5 internal score for long context and a 131K token window, it outperforms its overall rank in handling large datasets. Its strength in classification, tool calling, and structured output makes it a reliable engine for pipeline automation rather than creative or strategic reasoning.
The model is priced aggressively at a blended cost of $0.265/MTok, making it a high-value option for developers who need reliability in structured tasks without the cost of frontier models. However, this value is offset by significant weaknesses in safety calibration and persona consistency, suggesting it requires more rigorous prompt engineering or external guardrails to maintain a specific tone or safety profile.
Technical performance in complex reasoning is limited. While it handles basic classification well, its AIME 2025 score of 5.1% indicates a struggle with high-level mathematical and logical problems. It is a tool for extraction and organization, not for autonomous agentic planning or advanced strategic analysis.
Use this model if you need a low-cost solution for long-document analysis, tool integration, or structured data extraction. Skip this model if your application requires high safety precision, complex mathematical reasoning, or a consistent persona for user-facing interactions.
Strengths — Top 3
Relative weaknesses — Bottom 3
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